Toward Fully Neuromorphic Receivers for Ultra-Power Efficient Communications

TL;DR

This paper introduces a fully neuromorphic communication receiver that processes signals directly in the analog domain using spike-based computation, achieving ultra-low power consumption and improved error performance.

Contribution

It pioneers the design of a neuromorphic receiver that performs joint detection and decoding entirely with spiking signals, bypassing traditional digital processing overhead.

Findings

Achieves error-rate improvements over digital counterparts.

Operates with power consumption in the microwatt range.

Includes a noise-tracking mechanism for robustness.

Abstract

Neuromorphic computing, inspired by biological neural systems, has emerged as a promising approach for ultra-energy-efficient data processing by leveraging analog neuron structures and spike-based computation. However, its application in communication systems remains largely unexplored, with existing efforts mainly focused on mapping isolated communication algorithms onto spiking networks, often accompanied by substantial, traditional computational overhead due to transformations required to adapt problems to the spiking paradigm. In this work, we take a fundamentally different route and, for the first time, propose a fully neuromorphic communication receiver by applying neuromorphic principles directly in the analog domain from the very start of the receiver processing chain. Specifically, we examine a simple transmission scenario: a BPSK receiver with repetition coding, and show that we can achieve joint detection and decoding entirely through spiking signals. Our approach demonstrates error-rate performance gains over conventional digital realizations with power consumption on the order of microwatts, comparable with a single very low-resolution Analog-to-Digital Converter (ADC) utilized in digital receivers. To maintain performance under varying noise conditions, we also introduce a novel noise-tracking mechanism that dynamically adjusts neural parameters during transmission. Finally, we discuss the key challenges and directions toward ultra-efficient neuromorphic transceivers.